Quantum evolution parameter determination method and device, electronic device and medium

Abstract

The present disclosure provides a method, device, electronic device, computer-readable storage medium and computer program product for determining quantum evolution parameters, which relate to the computer field, and in particular to the field of quantum computer technology. The implementation scheme is: determining a first quantum circuit of n+m quantum bits, the first quantum circuit comprising a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits; inputting the determined initial quantum state into the first quantum circuit to obtain an output quantum state; measuring the output quantum state l times to obtain a measurement result and its probability distribution; determining the value of a loss function based on the measurement result and its probability distribution, the loss function being determined based on the covariance of the parameter to be determined; adjusting the value of the adjustable parameter to minimize the loss function; and determining the value of the parameter to be determined based on the first, second and third quantum neural networks and the initial quantum state after minimizing the loss function.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, in particular to the field of quantum computer technology, and specifically to a method, device, electronic device, computer-readable storage medium and computer program product for determining quantum evolution parameters. Background Art

[0002] The field of quantum computing has developed rapidly recently. From quantum algorithms, quantum hardware devices to quantum hardware and software integrated platforms, they are all moving steadily towards scale and practical application. More and more quantum algorithms and applications are emerging to solve problems that are difficult for classical computers to solve, showing the huge potential of quantum computers to surpass classical computers. Among them, solving practical problems through quantum computing and bringing quantum advantages is a direction that has attracted much attention. In this direction, how to use the entanglement nature of quantum computing to obtain an estimated accuracy that exceeds the existing classical precision measurement scheme with the same resource consumption is an important issue, which is called quantum precision measurement (quantum metrology). Summary of the invention

[0003] The present disclosure provides a method, device, electronic device, computer-readable storage medium and computer program product for determining quantum evolution parameters.

[0004] According to one aspect of the present disclosure, a method for determining quantum evolution parameters is provided, comprising: determining a first quantum circuit of n+m quantum bits, wherein the first quantum circuit comprises a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits, wherein the second quantum circuit is a quantum circuit corresponding to the quantum evolution, and wherein the first neural network is located before the second quantum circuit, the third quantum neural network is located after the second quantum circuit, and the second quantum neural network is located between the first quantum neural network and the third quantum neural network, wherein the second quantum circuit comprises parameters to be determined, and the first, second and third quantum neural networks comprise adjustable Parameters, wherein n is the number of quantum bits corresponding to the quantum evolution, m is the number of auxiliary quantum bits, and both n and m are positive integers; inputting the determined initial quantum state into the first quantum circuit to obtain an output quantum state; measuring the output quantum state l times to obtain a measurement result and its probability distribution, wherein l is a positive integer; determining a value of a loss function based on the measurement result and its probability distribution, wherein the loss function is determined based on the covariance of the parameter to be determined; adjusting the values ​​of the adjustable parameters of the first, second and third quantum neural networks to minimize the loss function; and determining the value of the parameter to be determined based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function.

[0005] According to another aspect of the present disclosure, a quantum evolution parameter determination device is provided, comprising: a first determination unit, configured to determine a first quantum circuit of n+m quantum bits, wherein the first quantum circuit comprises a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits, wherein the second quantum circuit is a quantum circuit corresponding to the quantum evolution, and wherein the first neural network is located before the second quantum circuit, the third quantum neural network is located after the second quantum circuit, and the second quantum neural network is located between the first quantum neural network and the third quantum neural network, wherein the second quantum circuit comprises parameters to be determined, and the first, second and third quantum neural networks comprise adjustable parameters, wherein n is the quantum circuit corresponding to the quantum evolution. The invention relates to a method for determining a quantum state of a first quantum network, wherein the first quantum circuit comprises a plurality of quantum bits, wherein m is the number of quantum bits, m is the number of auxiliary quantum bits, and n and m are both positive integers; an acquisition unit, configured to input the determined initial quantum state into the first quantum circuit to obtain an output quantum state; a measurement unit, configured to measure the output quantum state l times to obtain a measurement result and a probability distribution thereof, wherein l is a positive integer; a second determination unit, configured to determine a value of a loss function based on the measurement result and the probability distribution thereof, wherein the loss function is determined based on the covariance of the parameter to be determined; an optimization unit, configured to adjust the values ​​of the adjustable parameters of the first, second and third quantum neural networks to minimize the loss function; and a third determination unit, configured to determine the value of the parameter to be determined based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method described in the present disclosure.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method described in the present disclosure when executed by a processor.

[0009] According to one or more embodiments of the present disclosure, the concept of quantum neural network is introduced in the sequential system of quantum precision measurement, and the quantum neural network is used to adjust the parameters in the circuit to minimize the covariance of the unknown parameter estimation value. Thereby improving the accuracy of quantum precision measurement and estimation of parameters; and, by introducing auxiliary quantum bits to fully utilize the entanglement characteristics of quantum computing, quantum neural networks can be efficiently optimized without the need for a large circuit depth, and can also obtain higher measurement accuracy, thereby achieving the purpose of estimating unknown parameters with higher accuracy and efficiency in a noisy environment.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0012] Figure 1 A flow chart of a method for determining quantum evolution parameters according to an embodiment of the present disclosure is shown;

[0013] Figure 2 A schematic diagram of a quantum circuit containing quantum evolution to be measured according to an embodiment of the present disclosure is shown;

[0014] Figure 3 A partial schematic diagram of a quantum circuit including system noise and a quantum neural network according to an embodiment of the present disclosure is shown;

[0015] Figure 4 A schematic diagram comparing experimental results obtained according to an embodiment of the present disclosure and existing solutions is shown.

[0016] Figure 5 A structural block diagram of a device for determining quantum evolution parameters according to an embodiment of the present disclosure is shown; and

[0017] Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0019] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0020] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0021] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0022] So far, all the different types of computers in use are based on classical physics as the theoretical basis for information processing, and are called traditional computers or classical computers. Classical information systems use binary data bits, which are the easiest to implement physically, to store data or programs. Each binary data bit is represented by 0 or 1, called a bit or bit, as the smallest unit of information. Classical computers themselves have inevitable weaknesses: one is the most basic limitation of energy consumption in the computing process. The minimum energy required for logic elements or storage units should be several times more than kT to avoid misoperation due to thermal expansion and fall; the second is information entropy and heat energy consumption; the third is that when the wiring density of computer chips is very high, according to the Heisenberg uncertainty relation, when the uncertainty of the electron position is very small, the uncertainty of the momentum will be very large. Electrons are no longer bound, and there will be quantum interference effects, which may even destroy the performance of the chip.

[0023] A quantum computer is a physical device that follows the properties and laws of quantum mechanics to perform high-speed mathematical and logical operations, store and process quantum information. When a device processes and calculates quantum information and runs quantum algorithms, it is a quantum computer. Quantum computers follow the unique laws of quantum dynamics (especially quantum interference) to achieve a new mode of information processing. For parallel processing of computational problems, quantum computers have an absolute advantage in speed over classical computers. The transformation of each superposition component by a quantum computer is equivalent to a classical calculation. All these classical calculations are completed at the same time and superimposed according to a certain probability amplitude to give the output result of the quantum computer. This calculation is called quantum parallel computing. Quantum parallel processing greatly improves the efficiency of quantum computers, allowing them to complete tasks that classical computers cannot complete, such as factoring a large natural number. Quantum coherence is essentially used in all quantum ultra-fast algorithms. Therefore, quantum parallel computing, which replaces classical states with quantum states, can achieve computing speeds and information processing functions that are incomparable to classical computers, while saving a lot of computing resources.

[0024] With the rapid development of quantum computer technology, the application scope of quantum computers is becoming wider and wider due to its powerful computing power and fast running speed. Quantum evolution is a physical process of quantum system. In the fields of physics and chemistry, quantum evolution can be used to obtain the properties of physical systems or chemical molecules. Quantum evolution can be represented by the unitary operator U(φ), where φ=(φ1,…,φ k ) is a real number parameter that affects the properties of the quantum evolution. The purpose of quantum precision measurement is to complete the parameter estimation of the quantum evolution through information processing, transmission and sensing of the quantum state while having the ability to manipulate and measure the quantum state.

[0025] Quantum precision measurement provides a way to overcome the practical limitations of sensing devices. By utilizing the entangled nature of quantum bits, high-precision magnetic field sensors, gravity sensors, and atomic clocks can be designed. Therefore, quantum precision measurement can be used to measure physical quantities such as magnetic fields, gravitational waves, and temperature with high precision, and has potential applications in geophysics, medical imaging, and materials science.

[0026] For example, quantum precision measurement can be used in quantum communication, such as for quantum channel resolution to estimate the channel ε θ The parameter θ in (ρ)=θρ+(1-θ)I / 2. In quantum communication, the state of quantum bits needs to be measured to transmit information. Quantum precision measurement can help design and analyze quantum communication protocols, such as for the development of quantum key distribution protocols and solving security issues in quantum communication.

[0027] Quantum precision measurement can be used in quantum image processing. In this case, quantum states can represent pixels in an image. By measuring quantum states, information about the image can be obtained. Quantum image processing can also be used in applications such as image compression and encryption. Quantum precision measurement can be used in quantum biology, for example, using the entangled nature of quantum bits to study the folding state of proteins, study the interaction of molecules, etc.

[0028] Quantum precision measurement is a core application of quantum computing. It can expand the detection limits of existing physical quantities. Efficient quantum precision measurement solutions have good application prospects in time measurement, gravity measurement, molecular structure measurement, radar sensing, active and passive navigation, etc.

[0029] However, how to find a detection state that can estimate parameters with high precision in a noisy environment, a quantum circuit containing the quantum evolution to be measured, and a measurement basis remains an unsolved problem; secondly, the current schemes that can perform quantum precision measurement have high requirements in terms of quantum circuit width, etc. Therefore, for larger-scale systems, the current solutions are not practical due to excessive resource consumption.

[0030] Therefore, according to an embodiment of the present disclosure, a method for determining a quantum evolution parameter is provided. Figure 1 FIG. 4 shows a flow chart of a method for estimating quantum evolution parameters according to an embodiment of the present disclosure, as shown in FIG. Figure 1 As shown, method 100 includes: determining a first quantum circuit of n+m quantum bits, wherein the first quantum circuit includes a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits (step 110); inputting the determined initial quantum state into the first quantum circuit to obtain an output quantum state (step 120); measuring the output quantum state l times to obtain a measurement result and a probability distribution thereof, wherein l is a positive integer (step 130); determining a value of a loss function based on the measurement result and the probability distribution thereof, wherein the loss function is determined based on the covariance of the parameter to be determined (step 140); adjusting the values ​​of the adjustable parameters of the first, second and third quantum neural networks to minimize the loss function (step 150); and determining the value of the parameter to be determined based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function (step 160).

[0031] In the present disclosure, the second quantum circuit is a quantum circuit corresponding to the quantum evolution, and wherein the first neural network is located before the second quantum circuit, the third quantum neural network is located after the second quantum circuit, and the second quantum neural network is located between the first quantum neural network and the third quantum neural network, wherein the second quantum circuit includes parameters to be determined, and the first, second and third quantum neural networks include adjustable parameters, wherein n is the number of quantum bits corresponding to the quantum evolution, m is the number of auxiliary quantum bits, and n and m are both positive integers.

[0032] According to the embodiments of the present disclosure, the concept of quantum neural network is introduced into the sequential system of quantum precision measurement, and the quantum neural network is used to adjust the parameters in the circuit to minimize the covariance of the unknown parameter estimation value. Thereby improving the accuracy of quantum precision measurement and estimation of parameters; and, by introducing auxiliary quantum bits to fully utilize the entanglement characteristics of quantum computing, quantum neural networks can be efficiently optimized without the need for a large circuit depth, and can also obtain higher measurement accuracy, thereby achieving the purpose of estimating unknown parameters with higher accuracy and efficiency in a noisy environment.

[0033] Furthermore, based on the higher precision parameters estimated according to the embodiments of the present disclosure, the parameter θ in the quantum channel resolution described above can more accurately describe the channel information, thereby more accurately transmitting information based on the determined quantum channel information and improving the information transmission effect.

[0034] In some examples, the parameters to be estimated may also be image information, such as pixel information. Based on the higher-precision parameters estimated according to the embodiments of the present disclosure, the image information can be described more accurately, thereby enabling quantum image processing to be achieved with higher precision.

[0035] It is understandable that the process of quantum precision measurement usually includes the following three basic steps:

[0036] (1) Prepare the probe state: control the quantum system to a specific quantum state ρ0 through a control signal;

[0037] (2) Interaction: Prepare a quantum circuit containing one or more quantum evolutions to be measured U(φ) The detection state ρ0 is input into the quantum circuit as the input state and obtain the quantum circuit Output state

[0038] (3) Measurement: Based on the preset measurement basis M, the output state ρ after measurement is measured φ and obtain the measurement results.

[0039] It is understood that in the present disclosure, a quantum circuit containing a single or multiple quantum evolutions to be measured U(φ) can be represented as

[0040] Therefore, according to some embodiments, the second quantum circuit may be plural, and the number of the second quantum networks is the same as the number of the second quantum circuits.

[0041] It is understandable that the more the number of second quantum networks, the higher the accuracy of the parameters to be determined in the second quantum circuit that can be estimated, and the more accurate the channel information or image information described, so that information can be transmitted or image processing can be achieved more accurately based on the determined quantum channel information or image information; but at the same time, more parameters need to be optimized. Users can set the number of second quantum networks and second quantum circuits according to specific needs, and there is no restriction here. Furthermore, the random variable corresponding to the measurement result is denoted as x. After repeating the above process, the probability distribution of the random variable x can be obtained and denoted as p(x|φ), and then the parameter estimate of the quantum evolution is obtained according to the probability distribution p(x|φ).

[0042] According to some embodiments, the number of parameters to be determined is k, where k is a positive integer, and the loss function is determined based on the following formula:

[0043]

[0044] Among them, I(φ,θ) represents the classical Fisher information, Wherein, p(x|φ,θ) represents the probability distribution corresponding to the measurement result x obtained when the parameter to be determined is φ and the adjustable parameter is θ, φ i and φ j denote the i-th and j-th variables of the k parameters to be determined, tr is the trace function, W is the semi-positive definite matrix, Indicates the expected value.

[0045] In some examples, The covariance of The lower bound of can be determined by a k×k matrix called classical Fisher information, namely:

[0046]

[0047] Here, k is the number of parameters to be estimated.

[0048] Specifically, The relationship between the lower bound and I(φ) can be expressed by the Cramér–Rao bound, namely:

[0049]

[0050] Where l is the number of times the measurement process is executed; I(φ) -1 is the inverse matrix of the classical Fisher information I(φ); ≥ is a symbol representing a binary relationship, that is, for two square matrices M1 and M2 of the same size, it means that M1-M2 is a semi-positive definite matrix.

[0051] According to some embodiments, the loss function is further determined based on a preset semi-positive definite matrix, wherein the semi-positive definite matrix is ​​determined based on the weights of the parameters to be determined.

[0052] In some examples, to better describe The binary relationship between the lower bound and I(φ) can usually be introduced by introducing a weighted semi-positive definite matrix W such that:

[0053]

[0054] Among them, tr is the trace function, and the value of W is related to the importance of each parameter. For example, when each parameter is equally important, W is taken as the unit matrix. In short, C W The smaller (φ) is, The lower the lower bound of The higher the accuracy.

[0055] In some embodiments, the parameter to be determined may be a parameter obtained by substituting the parameter corresponding to the quantum evolution into a certain function. For example, when the unknown parameter itself is not to be estimated but the value f(φ) after substituting the unknown parameter into a certain function f(y), according to the same principle, we have:

[0056]

[0057] Here, y is the function's independent variable, is the estimated value of f(φ), I f (φ)=J T I(φ)J,J is the Jacobian matrix of f(φ), Likewise, The lower the lower bound of The higher the accuracy.

[0058] Therefore, to realize quantum precision measurement, given a real number parameter φ = {φ1,…,φ k}determined by a quantum evolution U(φ) and a weighting matrix W (or, also including the function f(x)), it is necessary to find a detection state ρ0, a measurement basis M, and a quantum circuit containing the quantum evolution , so that C W (φ)(or ) is minimized, making it possible to measure unknown parameters with high precision.

[0059] According to some embodiments, based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function, the value of the parameter to be determined is determined by a maximum likelihood estimation (MLE) method.

[0060] Specifically, in an example in which the first quantum circuit includes N quantum neural networks (including the first, second, and third quantum neural networks), a classical computer is used to calculate the optimal parameters θ according to the optimized parameters θ. * Computational Quantum Neural Networks i (θ *(i) )(i=0,…,N), we further get the first quantum circuit Relationship with unknown parameters And the output state obtained through the first quantum circuit The estimated value of the unknown parameter φ is estimated using the maximum likelihood estimation method * .

[0061] It is understandable that, although it is introduced that the maximum likelihood estimation method can be used to estimate unknown parameters, other appropriate methods can also be selected in actual use, and there is no limitation here.

[0062] According to some embodiments, the values ​​of the adjustable parameters of the first, second and third quantum neural networks are adjusted based on a gradient descent method to minimize the loss function.

[0063] It is understandable that although the gradient descent method can be used as a parameter update strategy, in actual use, other gradient-based or non-gradient-based optimization methods can be flexibly selected according to factors such as application scenarios, hardware environment, calculation accuracy or convergence speed, and no limitation is made here.

[0064] In an exemplary embodiment according to the present disclosure, given a parameterized quantum evolution 1(φ), its unknown parameter φ (or a function value f(φ) of a function f(y)) is determined. Figure 2 FIG. 1 shows a schematic diagram of a quantum circuit containing quantum evolution to be measured according to an embodiment of the present disclosure. Figure 2 As shown, for a quantum circuit containing the quantum evolution to be measured θ=(θ (0) ,θ (1) ,…,θ (N) ) is an adjustable parameter vector, U0(θ (0) ),U1(θ (1) ),…,U N (θ (N) ) is a quantum neural network, where ε(p) is the noise and p is the noise coefficient. Let the input state σ0 be Interact with each other, by continuously iteratively updating the parameters in the quantum neural network to minimize the loss function: (or ) Improve the accuracy of quantum precision measurement and estimation of parameters, and finally obtain a quantum circuit that can estimate unknown parameters with high accuracy

[0065] This solution can further improve the efficiency and accuracy of quantum precision measurement in noisy environments, thereby accelerating the implementation of quantum applications in fields such as physics and machine learning.

[0066] For example, based on the higher precision parameters estimated according to the embodiments of the present disclosure, the parameter θ in the quantum channel resolution described above can more accurately describe the channel information, thereby more accurately transmitting information based on the determined quantum channel information and improving the information transmission effect.

[0067] Alternatively, for example, the parameter to be estimated may also be image information, such as pixel information. Based on the higher precision parameters estimated according to the embodiments of the present disclosure, the image information may be described more accurately, thereby enabling quantum image processing to be achieved with higher precision.

[0068] Specifically, determine the quantum evolution U(φ) (whose quantum bit number is n) containing the parameter to be measured, the number k of the function to be measured φ, and the weighted semi-positive definite matrix W. Note that the initial quantum state The measurement basis M′={|j>} is preset. The appropriate auxiliary system size m and the number of measurements l are selected according to the number of quantum bits n and the actual quantum device conditions.

[0069] The selected quantum neural network template U0(θ (0) ),U1(θ (1) ),…,U N (θ (N) ) and the quantum evolution to be measured U(φ) according to Figure 2 Placed in a quantum circuit with n+m qubits Among them, φ=(φ1,φ2,…,φ k ) is the real number parameter to be measured, θ=(θ (0) ,θ (1) ,…,θ (N)) is an adjustable real number parameter, and the adjustable parameter θ is randomly initialized (i) (i=0…N) value.

[0070] In a quantum computer, the initial quantum state σ0 is put into the quantum circuit as the input state In the output state Measure the output state l times and get Here j (φ,θ) is the probability of obtaining the result j by measuring ρ. Let the random variable of measuring ρ be x, and its probability The probability distribution of the composition is denoted as p(x|φ,θ).

[0071] On a classical computer, according to the formula of classical Fisher information:

[0072]

[0073] Combined with the obtained probability distribution p(x|φ), we get the matrix I(φ,θ), and further calculate:

[0074]

[0075] Among them, tr is the trace function, W is the weighted semi-positive definite matrix, represents p(x|φ,θ) with respect to the variable φ i and φ j The partial derivative of .

[0076] Minimize C using gradient descent (or other optimization methods) on a classical computer W (φ,θ) improves the accuracy of estimated parameters. Update θ (i) (i=0…N), and execute steps 3-5 repeatedly until C W (φ,θ) reaches the convergence condition. Using a classical computer, according to the optimized parameter θ * Calculate U i (θ *(i) )(i=0,…,N), and further obtain the relationship between quantum circuit u and unknown parameters And the output state obtained through this circuit For example, the estimated value of the unknown parameter φ is obtained by maximum likelihood estimation * . Then output the estimated value of the unknown parameter φ * , Detection State Measurement base And the optimized quantum circuit

[0077] In one example according to the present disclosure, a Fourier amplitude function value capable of estimating the unknown parameter φ with high accuracy is given: First, we can set the circuit size to 6 qubits, where n = 3, m = 3, and set the unknown parameters to be determined to The quantum to be measured evolves into in represents the Pauli Z operator acting on the jth quantum bit, and the noise and quantum neural network respectively select the PhaseDamping and strongly entangled circuit templates in the simulator, such as Figure 3 As shown. Among them, Figure 3 The 3-qubit strongly entangled circuit is shown in Figure 1. U3 represents a universal single-qubit rotating gate. Each U3 gate contains three adjustable parameters. The function to be optimized is set as:

[0078]

[0079] Among them, I f (φ)=J T I(φ)J is the Jacobian matrix of f(φ),

[0080] Then, the experiment is run according to the scheme described in the above embodiment with the zero state as the input state, and the experimental results obtained by the scheme according to the embodiment of the present disclosure are compared with the existing schemes, such as Figure 4 As shown. The horizontal axis represents the noise coefficient and the vertical axis represents the optimized function C W (φ,θ), which is the highest accuracy that the estimated parameters can achieve. It can be seen that the C W (φ,θ) increases with the increase of p, which indicates that the estimation accuracy decreases with the increase of noise coefficient. W (φ,θ) is always smaller than the C of the existing solution. W (φ,θ). This shows that the accuracy of parameter estimation obtained after optimization according to the scheme of the embodiment of the present disclosure is always higher than the accuracy of parameter estimation of the existing scheme. Therefore, it can be concluded from the above case that, under the same noise environment, the accuracy of estimation according to the scheme of the embodiment of the present disclosure is better than the accuracy obtained by the existing scheme.

[0081] According to the embodiments of the present disclosure, Figure 5As shown, a quantum evolution parameter determination device 500 is also provided, including: a first determination unit 510, configured to determine a first quantum circuit of n+m quantum bits, wherein the first quantum circuit includes a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits, wherein the second quantum circuit is a quantum circuit corresponding to the quantum evolution, and wherein the first neural network is located before the second quantum circuit, the third quantum neural network is located after the second quantum circuit, and the second quantum neural network is located between the first quantum neural network and the third quantum neural network, wherein the second quantum circuit includes parameters to be determined, and the first, second and third quantum neural networks include adjustable parameters, wherein n is the number of quantum bits corresponding to the quantum evolution, and m is The number of auxiliary quantum bits, n and m are both positive integers; an acquisition unit 520, configured to input the determined initial quantum state into the first quantum circuit to obtain an output quantum state; a measurement unit 530, configured to measure the output quantum state l times to obtain a measurement result and a probability distribution thereof, wherein l is a positive integer; a second determination unit 540, configured to determine a value of a loss function based on the measurement result and its probability distribution, wherein the loss function is determined based on the covariance of the parameter to be determined; an optimization unit 550, configured to adjust the values ​​of the adjustable parameters of the first, second and third quantum neural networks to minimize the loss function; and a third determination unit 560, configured to determine the value of the parameter to be determined based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function.

[0082] Here, the operations of the above-mentioned units 510-560 of the quantum evolution parameter determination device 500 are similar to the operations of the above-described steps 110-160, and will not be repeated here.

[0083] According to an embodiment of the present disclosure, an electronic device, a readable storage medium and a computer program product are also provided.

[0084] refer to Figure 6, a block diagram of an electronic device 600 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0085] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0086] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 can be any type of device that can input information to the electronic device 600. The input unit 606 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 607 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 608 can include but is not limited to a disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0087] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0088] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0090] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0091] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0092] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0093] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0094] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be performed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document is not limited here.

[0095] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A method for determining quantum evolution parameters, comprising: Determine a first quantum circuit of n+m quantum bits, wherein the first quantum circuit includes a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits, wherein the second quantum circuit is a quantum circuit corresponding to the quantum evolution, and wherein the first neural network is located before the second quantum circuit, the third quantum neural network is located after the second quantum circuit, and the second quantum neural network is located between the first quantum neural network and the third quantum neural network, wherein the second quantum circuit includes parameters to be determined, and the first, second and third quantum neural networks include adjustable parameters, wherein n is the number of quantum bits corresponding to the quantum evolution, m is the number of auxiliary quantum bits, and n and m are both positive integers; Inputting the determined initial quantum state into the first quantum circuit to obtain an output quantum state; Measuring the output quantum state l times to obtain a measurement result and a probability distribution thereof, where l is a positive integer; Based on the measurement results and their probability distribution, determining a value of a loss function, wherein the loss function is determined based on the covariance of the parameters to be determined; adjusting the values ​​of the adjustable parameters of the first, second and third quantum neural networks to minimize the loss function; and Based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function, the value of the parameter to be determined is determined.

2. The method of claim 1, wherein: The loss function is also determined based on a preset semi-positive definite matrix, wherein the semi-positive definite matrix is ​​determined based on the weights of the parameters to be determined.

3. The method of claim 1, wherein: There are a plurality of the second quantum circuits, and the number of the second quantum networks is the same as the number of the second quantum circuits.

4. The method of claim 2, wherein: The number of parameters to be determined is k, where k is a positive integer, and the loss function is determined based on the following formula: Among them, I(φ,θ) represents the classical Fisher information, Wherein, p(x|φ,θ) represents the probability distribution corresponding to the measurement result x obtained when the parameter to be determined is φ and the adjustable parameter is θ, φ i and φ j denote the i-th and j-th variables of the k parameters to be determined, tr is the trace function, W is the semi-positive definite matrix, It means to find the expected value, and l is the number of executions of the measurement process.

5. The method of claim 1, wherein: Based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function, the value of the parameter to be determined is determined by a maximum likelihood estimation method.

6. The method of claim 1, wherein: The values ​​of the adjustable parameters of the first, second and third quantum neural networks are adjusted based on a gradient descent method to minimize the loss function.

7. A device for determining quantum evolution parameters, comprising: A first determining unit is configured to determine a first quantum circuit of n+m quantum bits, wherein the first quantum circuit includes a second quantum circuit acting on n quantum bits, and first, second and third quantum neural networks acting on n+m quantum bits, wherein the second quantum circuit is a quantum circuit corresponding to the quantum evolution, and wherein the first neural network is located before the second quantum circuit, the third quantum neural network is located after the second quantum circuit, and the second quantum neural network is located between the first quantum neural network and the third quantum neural network, wherein the second quantum circuit includes parameters to be determined, and the first, second and third quantum neural networks include adjustable parameters, wherein n is the number of quantum bits corresponding to the quantum evolution, m is the number of auxiliary quantum bits, and n and m are both positive integers; an acquisition unit, configured to input the determined initial quantum state into the first quantum circuit to obtain an output quantum state; A measuring unit configured to perform l measurements on the output quantum state to obtain a measurement result and a probability distribution thereof, wherein l is a positive integer; A second determination unit is configured to determine a value of a loss function based on the measurement result and its probability distribution, wherein the loss function is determined based on the covariance of the parameter to be determined; an optimization unit configured to adjust the values ​​of the adjustable parameters of the first, second and third quantum neural networks to minimize the loss function; and The third determining unit is configured to determine the value of the parameter to be determined based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function.

8. The device according to claim 7, wherein: The loss function is also determined based on a preset semi-positive definite matrix, wherein the semi-positive definite matrix is ​​determined based on the weights of the parameters to be determined.

9. The device according to claim 7, wherein: There are a plurality of the second quantum circuits, and the number of the second quantum networks is the same as the number of the second quantum circuits.

10. The device according to claim 8, wherein: The number of parameters to be determined is k, where k is a positive integer, and the loss function is determined based on the following formula: Among them, I(φ,θ) represents the classical Fisher information, Wherein, p(x|φ,θ) represents the probability distribution corresponding to the measurement result x obtained when the parameter to be determined is φ and the adjustable parameter is θ, φ i and φ j denote the i-th and j-th variables of the k parameters to be determined, tr is the trace function, W is the semi-positive definite matrix, It means to find the expected value, and l is the number of executions of the measurement process.

11. The device according to claim 7, wherein: Based on the first, second and third quantum neural networks and the initial quantum state obtained after minimizing the loss function, the value of the parameter to be determined is determined by a maximum likelihood estimation method.

12. The device of claim 7, wherein: The values ​​of the adjustable parameters of the first, second and third quantum neural networks are adjusted based on a gradient descent method to minimize the loss function.

13. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

15. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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